On Fundamentals of Global Systems Control Science (GSCS)

Globalization leads us towards dealing with very complex systems that consist of evolving, overlapping, and interacting “socio-technical fabrics”. An existing general systems control theory cannot cope with problems occurring in such systems. This chapter is, first of all, an attempt to present an entirely new approach to the adequacy of system model and reality, based on a causal correspondence between information and knowledge obtained from a reality and its model. Secondly, the chapter suggests two possible control loops: one is meant to improve the model and another is the way to attain a certain planned goal to be reached by our reality. Four doctrines are presented as the basic principles of general fuzzy systems control theory (GFSCT) aiming to deal with the real fuzzy systems operating and functioning in a multiple space-time coordinate system. The minimization of a certain potential V-function is considered as a universal principle for existence of each system in the real world. Moreover, decentralized stochastic control is proposed to improve our reality and guarantee its lifetime unlimited behavior with a proper degree of certainty and space-time stability.

[1]  Jerry M. Mendel,et al.  Perceptual Reasoning for Perceptual Computing: A Similarity-Based Approach , 2009, IEEE Transactions on Fuzzy Systems.

[2]  Jerry M. Mendel,et al.  Perceptual Computing: Aiding People in Making Subjective Judgments , 2010 .

[3]  Alois Ferscha,et al.  Collective adaptive systems , 2015, UbiComp/ISWC Adjunct.

[4]  F. Malika Knowledge management and information technology , 2014, 2014 4th International Symposium ISKO-Maghreb: Concepts and Tools for knowledge Management (ISKO-Maghreb).

[5]  Anne E. Trefethen,et al.  From Data Analysis and Visualization to Causality Discovery , 2011, Computer.

[6]  George J. Klir,et al.  Architecture of Systems Problem Solving , 1985, Springer US.

[7]  Kumpati S. Narendra,et al.  Adaptation and learning in automatic systems , 1974 .

[8]  Yuqiong Liu,et al.  Reconciling theory with observations: elements of a diagnostic approach to model evaluation , 2008 .

[9]  Shane M. Sherlund,et al.  Making Sense of the Subprime Crisis , 2008 .

[10]  Naresh K. Sinha,et al.  Modern Control Systems , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[12]  Unfccc Kyoto Protocol to the United Nations Framework Convention on Climate Change , 1997 .

[13]  Raimundas Jasinevitchius Parallel space–time structure for computer vision systems , 1992 .

[14]  V. Borkar Stochastic Approximation: A Dynamical Systems Viewpoint , 2008 .

[15]  Hussein A. Abbass,et al.  Mebra: multiobjective evolutionary-based risk assessment , 2009, IEEE Computational Intelligence Magazine.

[16]  Yew-Soon Ong,et al.  Memetic Computation—Past, Present & Future [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[17]  MARK R. GARDNER,et al.  Connectance of Large Dynamic (Cybernetic) Systems: Critical Values for Stability , 1970, Nature.

[18]  Norbert Wiener,et al.  Cybernetics, or control and communication in the animal and the machine, 2nd ed. , 1961 .

[19]  Slawomir Zadrozny,et al.  Computing With Words Is an Implementable Paradigm: Fuzzy Queries, Linguistic Data Summaries, and Natural-Language Generation , 2010, IEEE Transactions on Fuzzy Systems.

[21]  Norbert Wiener,et al.  Cybernetics: Control and Communication in the Animal and the Machine. , 1949 .

[22]  A. Brabazon,et al.  An Introduction to Evolutionary Computation in Finance , 2008, IEEE Computational Intelligence Magazine.

[23]  K.J.R. Liu,et al.  Genomic processing for cancer classification and prediction - Abroad review of the recent advances in model-based genomoric and proteomic signal processing for cancer detection , 2007, IEEE Signal Processing Magazine.

[24]  Lotfi A. Zadeh Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift , 2008 .